XGBoost Algorithm for Telecom Customer Churn Prediction and Its Business Implications

Abstract

Predicting customer loss is a critical challenge for telecommunications companies, as identifying at-risk customers is key to reducing financial losses and formulating effective retention strategies. Conventional statistical methods and basic ensemble models often perform poorly on telecom datasets due to their inability to capture complex feature relationships and handle class imbalance. This study applies the Extreme Gradient Boosting (XGBoost) algorithm to telecom customer churn prediction, comparing its performance with Logistic Regression and Random Forest using the public Telco Customer Churn dataset (7,043 records, 21 features). A rigorous data preprocessing pipeline was implemented, including missing value handling, categorical encoding, and feature standardization, with an 80/20 train-test split that preserved class distribution. Hyperparameter tuning for XGBoost addressed the 26.5% churn rate imbalance. Evaluation metrics (accuracy, precision, recall, F1- score, AUC) showed XGBoost outperformed benchmark models: 82.1% accuracy, 78.3% churn recall, 59.2% precision, 67.4% F1-score, and 0.869 AUC. Feature importance analysis identified customer tenure, contract type, monthly charges, and fiber optic internet service as the primary churn drivers. XGBoost also captured complex non-linear interactions unrecognized by other models.